American Journal of Epidemiology Vol. 144, No. 3: 214-223
Copyright © 1996 by The Johns Hopkins University School of Hygiene and Public Health
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Validation of a Method to Estimate Age-specific Human Immunodeficiency Virus (HIV) Incidence Rates in Developing Countries Using Population-based Seroprevalence Data
1AJDSCAP Project, Family Health International, Arlington, VA
2Family Health International Durham, NC
3Department of Biostatistics and Epidemiology, Tulane University School of Public Health and Tropical Medicine New Orleans, LA
4Projet National de Lutte Contre le SIDA, Ministry of Health Bujumbura, Burundi
Correspondence to AIDSCAP Project, Family Health International, 2101 Wilson Blvd., Suite 710, Arlington, VA 22201.
The authors have conducted an analysis to validate a computer model that uses age-specific human immunodeficiency virus (HIV) prevalence data to estimate age-specific HIV incidence rates. Data for the analysis are from a cohort study of volunteer male workers in Bujumbura, the capital city of Burundi. Testing for HIV prevalence was conducted at baseline, and HIV-negative subjects were retested annually from 1990 to 1993 to determine rates of seroconversion. Input parameters required for the model include age-specific HIV prevalence and estimates of age-specrfic mortality rates for HIV-negative and HIV-positive subjects. Incidence rate estimates from the model were 2.0, 2.7, 1.0, 1.5, and 1.8 per 100 person-years for age groups 20–24, 25–29, 30–34, 35–39, and 40–44 years, respectively. Corresponding observed incidence rates for the same age groups were 1.6, 1.8, 2.2, 2.3, and 1.5 per 100 person-years, respectively. Most observed incidence rates fell within the 95% confidence limits of the model estimates. Expected numbers of cases within age intervals did not differ significantly from observed numbers of cases. The authors conclude that the model proved to be successful in approximating observed incidence rates and that it is a useful tool, particularly in countries where prevalence data are available and where HIV prevalence has stabilized, which is when the underlying assumptions in the model are best met. The model provides crucial information about incidence rates that might not be evident from prevalence data alone. Am J Epidemiol 1996; 144: 214–23.
cohort studies; developing countries; HIV seropositivity; HIV seroprevalence; incidence; models; statistical
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